U.S. patent application number 14/345235 was filed with the patent office on 2015-02-12 for load forecasting from individual customer to system level based on price.
This patent application is currently assigned to AUTOGRID INC.. The applicant listed for this patent is Vijay Srikrishna Bhat, Scott Christopher Locklin, Amit Narayan, Henry Schwarz. Invention is credited to Vijay Srikrishna Bhat, Scott Christopher Locklin, Amit Narayan, Henry Schwarz.
Application Number | 20150046221 14/345235 |
Document ID | / |
Family ID | 47178836 |
Filed Date | 2015-02-12 |
United States Patent
Application |
20150046221 |
Kind Code |
A1 |
Narayan; Amit ; et
al. |
February 12, 2015 |
LOAD FORECASTING FROM INDIVIDUAL CUSTOMER TO SYSTEM LEVEL BASED ON
PRICE
Abstract
The present invention relates to system and method for providing
near real-time DR events and price signals to the customer
end-points to optimally manage the available DR resources. The
system utilizes bottom up load forecasting for accurate
individualized forecasts for customer loads in the presence of
dynamic pricing signals. For better efficiency and reliability of
grid operation the system utilizes advanced machine learning and
robust optimization techniques for real-time and "personalized"
DR-offer dispatch.
Inventors: |
Narayan; Amit; (Cupertino,
CA) ; Locklin; Scott Christopher; (Berkeley, CA)
; Bhat; Vijay Srikrishna; (San Francisco, CA) ;
Schwarz; Henry; (Foster City, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Narayan; Amit
Locklin; Scott Christopher
Bhat; Vijay Srikrishna
Schwarz; Henry |
Cupertino
Berkeley
San Francisco
Foster City |
CA
CA
CA
CA |
US
US
US
US |
|
|
Assignee: |
AUTOGRID INC.
Redwood Shores
CA
|
Family ID: |
47178836 |
Appl. No.: |
14/345235 |
Filed: |
September 14, 2012 |
PCT Filed: |
September 14, 2012 |
PCT NO: |
PCT/US2012/000398 |
371 Date: |
October 13, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61535949 |
Sep 17, 2011 |
|
|
|
61535946 |
Sep 17, 2011 |
|
|
|
Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
Y04S 50/14 20130101;
H02J 3/008 20130101; G06Q 30/0204 20130101; Y04S 50/10 20130101;
G06Q 10/06 20130101; H02J 3/003 20200101; Y04S 10/50 20130101; G06Q
30/0202 20130101; G06Q 50/06 20130101 |
Class at
Publication: |
705/7.31 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/06 20060101 G06Q050/06 |
Claims
1. A method for individualized forecast of customer load in
presence of dynamic pricing signals comprising: recording the
customer's participation history in different demand response
events at each customer locations; segmenting the demand response
specific data in a plurality of related time series; building a
self-calibrated model for each customer using the time series;
taking feedback from the time series to predict the changes in
customer load profile; forecasting load usage and load shed as well
as error distribution associated with forecast using machine
learning and data mining techniques.
2. The method of claim 1 wherein the demand response event specific
data includes demand response resources data, its type, its
locations, characteristics such as response time, ramp time,
utility meter data, user specific data, time series data,
seasonality data, price index data, notification time requirement,
number of events in a particular period of time and number of
consecutive event, user preference to participate in the event,
price index and other regression based data.
3. The method of claim 1 wherein demand response specific data is
segmented on the basis of seasonality, time of occurrence, price
index, temperature and other regression parameters.
4. The method of claim 1 wherein the segmenting techniques used for
segmenting the demand response event specific data includes K-mean
and fuzzy K-means algorithm.
5. The method of claim 1 wherein pricing signals are variable on
current conditions and advanced notice requirements associated with
a demand response event.
6. The method of claim 1 wherein the forecasting of load is
performed as a function of time of day, weather and price
signal.
7. The method of claim 1 wherein the self-calibrated model will be
able to forecast shed capacity, ramp time and rebound effect for
the customer.
8. The method of claim 1 wherein the pricing signals include cost,
reliability, loading order, preference, GHG etc.
9. The method of claim 1 wherein the feedback is provided through
machine learning techniques.
10. The method of claim 1 wherein the participation history is
collected through advanced metering infrastructures and sensors
installed on the grid distribution.
11. The method of claim 1 wherein the machine learning algorithm
includes ARIMAX, KNN, SVM or Artificial Neural Network or a
combination thereof.
12. A method for individualized forecast of customer load in
presence of dynamic price signals comprising: Collecting a periodic
electricity usage data at each customer level; aggregating the
electricity usage data at transformer, feeder and sub-station
level; creating a customer profile for electric load usage with a
function of price elasticity, the said price elasticity function is
estimated using a machine learning technique; segmenting the
customer electricity usage data in time series using clustering
techniques; forecasting the electricity load usage for each
customer and the aggregated load usage at feeders, transformers and
substation level.
13. The method of claim 12 wherein the dynamic price signals
include price based DR for load forecasting.
14. The method of claim 12 wherein the pricing signals include
cost, reliability, loading order, preference, GHG etc.
15. The method of claim 12 wherein the participation history
signifies history of participation in past event, strategy for
reducing participation in high price event, notification time
requirements.
16. The method of claim 12 wherein the profile for individual
customer is generated on the basis of electric usage at the
end-level.
17. The method of claim 12 wherein the machine learning techniques
include ARIMAX, KNN, SVM or Artificial NeuralNetwork or a
combination thereof.
18. The method of claim 12 wherein the clustering techniques are
used to segment the usage data in similar time series on the basis
of seasonality, time of occurrence, price index, temperature and
other variables.
19. The method of claim 12 wherein the segmenting techniques used
for segmenting the demand response event specific data includes
K-means and fuzzy K-means methods.
20. The method of claim 12 wherein the aggregated power load is
calculated as the sum of forecast of individual customer.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application claims the benefit of priority to U.S.
Provisional Patent Application No. 61/535,949, filed Sep. 17, 2011,
entitled "Bottom-up Load Forecasting from Individual Customer to
System-Level Based on Price" and claims the benefit of priority to
U.S. Provisional Patent Application No. 61/535,946, filed Sep. 17,
2011, entitled "Machine Learning Applied to Smart Meter Data to
Generate User Profiles-Specific Algorithms", the contents of each
of which are hereby incorporated by reference in their
entireties.
FIELD OF THE INVENTION
[0002] The present invention relates generally to load forecasting,
and more particularly to bottom-up load forecasting from individual
customer to system level based on price.
BACKGROUND
[0003] Accurate models for electric power load forecasting are
essential for the operation and planning of a utility company. Load
forecasting helps an electric utility company to make important
decisions including decisions on purchasing and generating electric
power, load switching, and infrastructure development. Load
forecasts are extremely important for energy suppliers, ISOs,
financial institutions, and other participants in electric energy
generation, transmission, distribution, and markets. Load forecasts
can be divided into three categories: short-term forecasts which
are usually from one hour to one week, medium forecasts which are
usually from a week to a year, and long-term forecasts which are
longer than a year.
[0004] End-use or bottom up approach is used for generating medium
term forecasting. Bottom up approach directly estimates energy
consumption by using extensive information on end use and end
users, such as appliances, the customer use, their age, sizes of
houses, and so on. These models focus on the various uses of
electricity in the residential, commercial, and industrial sector.
These models are based on the principle that electricity demand is
derived from customer's demand for light, cooling, heating,
refrigeration, etc. Thus, end-use or bottom up models explain
energy demand as a function of the number of appliances and the
level of energy service demanded or work demand from each appliance
or system.
[0005] For load forecasting several factors should be considered,
such as time factors, weather data, possible customer's classes,
price signals, the historical load and weather data, the number of
customers in different categories, the appliances in the area and
their characteristics including age, the economic and demographic
data and their forecasts, the appliance sales data, and other
factors.
[0006] Recently, price signals are being considered for load
forecasting. A price signal is a message sent to consumers and
producers in the form of a price charged for a commodity; this is
seen as indicating a signal for producers to increase supplies
and/or consumers to reduce demand. However, existing load
forecasting systems were not able to do accurate individualized
forecast for customer loads in the presence of dynamic price
signals due to lack of usage information at the end customer
level.
[0007] In light of the foregoing discussion, forecasting algorithms
were needed that could account for individual customer price
elasticity during load forecasting.
ABBREVIATION AND DEFINITION
[0008] DROMS-RT: Demand Response Optimization and Management System
for Real-Time
[0009] DR: Demand Response
[0010] FE: Forecasting Engine
[0011] ML: Machine Learning
[0012] BE: Baseline Computation and Settlement Engine
[0013] KNN: K-nearest Neighbor
[0014] SVM: Support Vector Machine
[0015] DROMS-RT: DROMS-RT is a highly distributed demand response
optimization and management system for real-time power flow control
to support large scale integration of distributed generation into
the grid.
[0016] Demand Response (DR): Demand Response (DR) is a mechanism to
manage customer consumption of electricity in response to supply
conditions. DR is generally used to encourage consumers to reduce
demand, thereby reducing the peak demand for electricity.
[0017] Forecasting Engine (FE): Forecasting Engine (FE) gets the
list of available resources from the resource modelers; its focus
is to perform short-term forecasts of aggregate load and available
load-sheds for individual loads connected to DROMS-RT.
[0018] Machine Learning (ML): Machine learning (ML) is a subset of
artificial intelligence, and is concerned with the design and
development of algorithms that allow computers to evolve behavior
based on the data received from sensors and databases. Machine
learning techniques involve online learning which learn one
instance at a time.
[0019] Baseline Computation and Settlement Engine (BE): Baseline
Computation and Settlement Engine (BE) uses signal processing
techniques to identify even small systematic load sheds in the
background of very large base signals.
[0020] K-nearest Neighbor (KNN): A memory-based technique where
forecasts are generated by looking at the observed loads for
similar cases in the historical data.
[0021] Support Vector Machine (SVM): It is a curve fitting
technique that is relatively immune to noise, and can robustly
model non-linear relationships in the data by transforming the raw
data to higher dimensions.
SUMMARY OF THE INVENTION
[0022] Accordingly in an aspect of the present invention a method
for individualized forecast for customer load in presence of
dynamic pricing signals and optimal dispatch of DR resources across
a large portfolio of heterogeneous load in a demand response
management system is provided. The method comprises of keeping a
unified view of available demand side resources under all available
DR programs; recording history of participation in different DR
events at individual customer locations in a storing database;
segmenting the demand response specific data in a number of time
series that are related to each other; building a self-calibrated
model for each customer using historical time series data;
collecting periodic electricity usage data at individual customer
location; predicting the changes in customer load profile by
getting feedback from load time series of individual customers;
forecasting individual customer load usage and load shed as well as
error distribution associated with forecast using machine learning
and data mining techniques; getting continuous feedback from the
client device to increase the ability to forecast; dispatching the
DR signals across a portfolio of customers based on the forecasts
dependent on a cost function.
[0023] In another aspect of the present invention a method for
individualized forecast for customer forecast in presence of
dynamic price signals is provided. The method comprises of a
storing database for collecting periodic electricity usage data at
individual customer level using advance metering data and sensors
on distribution grid; aggregating the customer level data at
transformer, feeder and sub-station level; creating a profile of
electric load for individual customer on the basis of customer
price elasticity estimated using a plurality of machine learning
techniques; an open source software framework to support the
multiple machine learning models; segmenting the individual
customer load and usage data in time series using machine learning
models; producing short-term forecast for individual customer load
and aggregated power load as well as the error distribution
associated with the forecast.
BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The preferred embodiment of the invention will hereinafter
be described in conjunction with the appended drawings provided to
illustrate and not to limit the scope of the invention, wherein
like designation denote like element and in which:
[0025] FIG. 1 is a block diagram illustrating the operation of
demand response optimization and management system for real time
(DROMS-RT) in accordance with an embodiment of the present
invention.
[0026] FIG. 2 is a user interface showing the available demand side
resources under all available DR programs in accordance with an
embodiment of the present invention.
[0027] FIG. 3 user interface showing available demand side
resources under all available DR programs and history of
participation in different DR events at individual customer
locations in accordance with an embodiment of the present
invention.
[0028] FIG. 4 is a is a user interface showing the forecast for
individual customer load and aggregated power load in accordance
with an embodiment of the present invention.
[0029] FIG. 5 is a quick back-of-the-envelope calculation for the
size of the data in accordance with an embodiment of the present
invention.
[0030] FIG. 6 is a schematic representation of dynamic demand
response (DR) resource model inputs and portfolio of dynamic demand
response (DR) resources in accordance with an embodiment of the
present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0031] In the following detailed description of embodiments of the
invention, numerous specific details are set forth in order to
provide a thorough understanding of the embodiment of invention.
However, it will be obvious to a person skilled in art that the
embodiments of invention may be practiced without these specific
details. In other instances well known methods, procedures and
components have not been described in detail so as not to
unnecessarily obscure aspects of the embodiments of the
invention.
[0032] Furthermore, it will be clear that the invention is not
limited to these embodiments only. Numerous modifications, changes,
variation, substitutions and equivalents will be apparent to those
skilled in the art without parting from the spirit and scope of the
invention.
[0033] DROMS-RT is a highly distributed Demand Response
Optimization and Management System for Real-Time power flow control
to support large scale integration of distributed generation into
the grid.
[0034] Bottom-up load forecasting based on price is a technique
that uses the DROMS-RT system to forecast a model that takes into
account customer specific behavior and is able to predict the
change in load profile due to the use of specific strategies that
the customer might be using to shift the load from a period of high
prices to low prices. DROMS-RT automatically selects the mix of DR
resources best suited to meet the needs of the grid.
[0035] Bottom-up load forecasting based on price uses DROMS-RT
algorithm for accurate individualized forecasts for customer loads
in the presence of dynamic pricing signals. Dynamic pricing signals
include price based DR for load forecasting to shift peak load,
target loads within subLAPS (load aggregation points) and enable
valuable management of congestion constrained electric grids with
subLAP (load aggregation point) granularity by increasing the
overall peak demand.
[0036] For the purpose of load forecasting, the DROMS-RT provides
near real-time DR events and price signals to the customer
end-points to optimally manage the available DR resources. It uses
shelf information and communication technology and controls
equipment for DR purposes. For better efficiency and reliability of
grid operation DROMS-RT utilizes advanced machine learning and
robust optimization techniques for real-time and "personalized" DR
offer dispatch.
[0037] In bottom-up load forecasting, DROMS-RT will keep a unified
view of available demand side resources under all available DR
programs and history of participation in different DR events at
individual customer locations. The DR resource models will be
dynamic, meaning they will vary based on current conditions and
various advanced notice requirements. It uses historical time
series data from the past participation to build a self-calibrated
model for each customer that will be able to forecast shed
capacity, ramp time and rebound effects for that customer given the
time-of-day, weather and price signal.
[0038] The present invention relates to a system and a method for
bottom-up load forecasting from individual customer to system-level
based on price by utilizing DROMS-RT's forecasting algorithm that
accounts for individual customer price elasticity during load
forecasting. The DROMS-RT load forecast model also takes into
account customer specific behavior and is able to predict the
change in load profile due to the use of specific strategies that
the customer might be using to shift the load from a period of high
prices to low prices. Such strategies may include pre-cooling a
building in anticipation of high prices to reduce the usage during
the higher priced events. Such strategies are implicitly `learned`
from the load time-series of individual customers using machine
learning algorithm.
[0039] The DROMS-RT load forecast algorithm can predict effects of
customer fatigue when the high price events happen repeatedly,
back-to-back or if they are too long, by looking at the customer
load data. The availability of the individual load forecasts also
improves the overall accuracy of the system-level load forecast by
combining many different bottom-up sources of data.
[0040] Advanced Metering Infrastructure (AMI) and other types of
sensors on the distribution grid are used for collecting periodic
electricity usage data at an individual customer level and the
collected data is aggregated at the transformer, feeder and
sub-station level. The DROMS-RT system proposes to utilize this AMI
meter data and other data collected at the transformer or the
appliance level to forecast individual customer usage using machine
learning and time-series data mining techniques.
[0041] In an embodiment of the present invention, the system is
comprised of a novel forecasting engine based on modern online
machine learning algorithms that is designed to enable accurate
individualized forecasts for customer loads in the presence of
dynamic pricing signals, and a real-time decision engine will
enable continuous optimization and optimal dispatch of DR resources
across a large portfolio of heterogeneous loads that respond at
varying time-scales.
[0042] The individual time-series of customer load and usage data
is used to produce short-term forecasts for individual customer
loads as well as aggregated power load which is based on sums of
forecasts of individual customers. In addition, by creating
forecasts of individual customers, the utility company will be in a
better position to anticipate and geographically pinpoint load
imbalances and can take actions to mitigate such imbalances with
greater accuracy and efficiency.
[0043] Clustering techniques are used to segment the data into time
series that are associated with one another. The segmentation of
demand response specific data is carried out on the basis of
seasonality, time of occurrence, price index, temperature and other
regression parameters and the segmenting techniques or clustering
techniques used for segmenting the demand response event specific
data includes K-mean and fuzzy K-means algorithms. Segmentation of
the time series may also take place within a given time series.
[0044] Machine learning techniques are used for generating accurate
forecasts of baseline loads and load sheds in the presence of
demand response events, estimates of error distributions,
distributing massive amount of data, self learning and improving
the forecast accuracy. The Demand Response Optimization and
Management System for Real-Time (DROMS-RT) stores available demand
side resources and history of participation in different demand
response (DR) events at individual user locations in a storing
database such as MonetDB, KDB, or Xenomorph. By using this
information a virtual profile for each user can be built that is
able to forecast the load shed, shed duration, and reverse effects
for that user provided the time of day, weather and price signals
are known. These profiles are random in nature and capture the
individual user variances.
[0045] The machine learning model includes ARIMAX model,
memory-based machine learning models such as K-nearest neighbor,
fitted machine/connectionist learning models such as support vector
machine or artificial neural nets and the storing database includes
MonetDB, KDB, Xenomorph. The ARIMAX model can be built for
forecasting and characterization of customer response to demand
response signals with the grid using the clustered load data time
series. SVM or artificial neural network techniques are built to
produce accurate results in cases where there is much data, a
situation that fits the DROMS-RT problem very well.
[0046] Massively parallel implementations involving
Hadoop/Map-Reduce will be deployed to handle terabytes of data and
millions of data streams simultaneously. Time-series databases and
machine learning algorithms uses massively parallel and distributed
computation paradigm for handling large data using dimensionality
reduction.
[0047] The time series are multi-seasonal on at least three levels
that include time of day, day of week, and day of year seasonality,
as well as customer price sensitivity to scheduled demand response
(DR) events. DROMS-RT, by providing a unified view of all DR
resources across all programs and optimally dispatching these
resources will make the system significantly more efficient. This
efficiency gain will bring down the cost of electricity for
customers and will further spur adoption of the technology causing
a powerful positive feedback loop.
[0048] FIG. 1 is a schematic representation showing the operation
of demand response optimization and management system for real time
in accordance with an embodiment of the present invention.
Referring to FIG. 1, a demand response optimization and management
system for real time (DROMS-RT) 100 is provided. The system 100
comprising: a Forecasting Engine 104, a Baseline Engine 106, a
Resource Modeler 108, an Optimizer 110, and a Dispatch Engine 112.
The system 100 is coupled to the utility's backend data system 102
on one side and customer end-points 114 on the other side.
[0049] The system provides near real time DR event and price
signals to the customer end points to optimally manage the
available DR resources. The DR Resource Modeler (DRM) 108 within
the system 100 keeps track of all the available DR resources, their
types, their locations and other relevant characteristics such as
response times, ramp-times etc. The Forecasting Engine (FE) 104
gets the list of available resources from the DR Resource Modeler
108. The focus of the Forecasting Engine 104 is to perform
short-term forecasts of aggregate load and available load-sheds for
individual loads connected to the system 100. The Optimizer 110
takes the available resources and all the constraints from the DR
Resource Modeler 108 and the forecasts of individual loads and
load-sheds and error distributions from the Forecasting Engine 104
to determine the optimal dispatch of demand response under a given
cost function. The Baseline Engine 106 uses signal processing
techniques to identify even small systematic load sheds in the
background of very large base signals. The system 100 is coupled to
customer data feed 114 on one side for receiving live data-feeds
from customer end-devices. The system is coupled to utility data
feed 102 on another side and the data from the utility data feed
102 is provided to calibrate the forecasting and optimization
models to execute demand response events. The system 100 has a
Dispatch Engine 112 that helps in taking decision and uses these
resource specific stochastic models to dispatch demand response
signals across a portfolio of customers to generate ISO bids from
demand response or to optimally dispatch demand response signals to
the customer based on the cleared bids and other constraints of the
grid. The system uses customer/utility interface 116 connected to
baseline engine 108 that provides an interface between the system
and customer or the utility.
[0050] FIG. 2 is a user interface showing the available demand side
resources under all available DR programs in accordance with an
embodiment of the present invention. In practice, of course, some
of the feeds might not be available all the time or in real-time;
the Forecasting Engine 104 is also able to run in an "off-line"
manner or with partial data feeds in these cases. The goal of the
system 100 is to provide near real-time demand response event and
price signals to the customer end-points to optimally manage the
available demand response resources.
[0051] FIG. 3 is a user interface showing available demand side
resources under all available DR programs and history of
participation in different DR events at individual customer
locations in accordance with an embodiment of the present
invention.
[0052] The DR Resource Modeler 108 continuously updates the
availability of resources affected by commitment to or completion
of an event. The DR Resource Modeler 108 also monitors the
constraints associated with each resource such as the notification
time requirements, number of events in a particular period and
number of consecutive events. It can also monitor user preferences
to determine a "loading order" as to which resources are more
desirable for participation in demand response events from a
customer's perspective, and the contract terms the price at which a
resource is willing to participate in an event. The demand response
Resource Modeler 108 also gets a data feed from the client to
determine whether the client is "online" (i.e. available as a
resource) or has opted-out of the event.
[0053] FIG. 4 is a user interface showing the forecast for
individual customer load and aggregated power load in accordance
with an embodiment of the present invention. The Forecasting Engine
104 accounts for a number of explicit and implicit parameters and
applies machine learning (ML) techniques to derive short-term load
and shed forecasts as well as error distributions associated with
these forecasts. The Forecasting engine 104 provides baseline
samples and the error distribution to the Baseline Engine 106. In
addition, the Baseline Engine 106 gets the data feeds from the
meter which is the actual power consumption data.
[0054] The Forecasting Engine 104 provides baseline samples and the
error distribution to the BE engine 106. BE engine 106 uses signal
processing techniques to identify even small systematic load sheds
in the background of very large base signals and verifies whether a
set of customers have all met their contractual obligation in terms
of load-sheds. The BE 106 uses `event detection` algorithm to
determine if the load actually participated in the DR event, and if
so, what the demand reduction due to that event was. The BE engine
106 feeds data back to the Forecasting Engine (FE) 104 so that it
can be used to improve the baseline forecast. The Forecasting
Engine 104 can also update the demand resource modeler (DRM) 104
about the load preferences by implicitly learning what type of
decisions the client devices are making to the DR event offers.
[0055] The Optimization Engine (OE) 110 takes the available
resources and all the constraints from the DRM (Demand Resource
Modeler) 104 and the forecasts of individual loads and load-sheds
and error distributions from the FE 104 to determine the optimal
dispatch 112 of DR under a given cost function. OE 110 can
incorporate a variety of cost functions such as cost, reliability,
loading order preference, GHG or their weighted sum and can make
optimal dispatch decisions over a given time-horizon that could
cover day-ahead and near real-time horizons simultaneously. The
system 100 will be able to automatically select the mix of DR
resources best suited to meet the needs of the grid such as peak
load management, real-time balancing, regulation and other
ancillary services. The OE 110 can also be used to generate bids
for wholesale markets given the information from DRM 104, and the
wholesale market price forecasts that can be supplied
externally.
[0056] The Dispatch Engine 112 dispatches the optimal demand
response (DR) services in timeframes suitable for providing
ancillary services to the transmission grid.
[0057] FIG. 5 is a quick back-of-the-envelope calculation for the
size of the data. Referring to FIG. 5, the DROMS-RT Forecasting
Engine (FE) 106 produces short-term forecasts for individual
customer loads as well as aggregated power load, based on sums of
forecasts of individual customers. While the availability of the
individual customer data should provide more accurate results, the
sheer size of the data makes for interesting engineering
challenges. A quick back-of-the-envelope calculation for the size
of the data shows that it can grow up to several petabytes with
only a few million smart meters collecting 15-minute interval
data.
[0058] FIG. 6 illustrates dynamic DR Resource Model inputs and
portfolio of dynamic DR resources in accordance with an embodiment
of the present invention. The figure illustrates the various inputs
to the dynamic demand response resource model 602 that are input to
dynamic demand response resource model (unique per load) 604 and
portfolio of dynamic demand response resources 606 controlled by
the demand response optimization and management system for real
time to produce pseudo generation per utility/ISO signal.
[0059] The DR Resource model 604 is a dynamic means that will vary
based on the current conditions and various advanced notice
requirements. The DR Resource Modeler (DRM) 108 within DROMS-RT
keeps track of all the available DR Resources, their types, their
locations and other relevant characteristics such as response
times, ramp-times etc. The DRM 108 also monitors the constraints
associated with each resource such as the notification time
requirements, number of events in a particular period of time and
number of consecutive events. It can also monitor user preferences
to determine a "loading order" as to which resources are more
desirable for participation in DR events from a customer's
perspective and the contract terms and price at which a resource is
willing to participate in an event. The DRM 108 also gets a data
feed from the client to determine if the client is "online" (i.e.
available as a resource) and whether the client has opted-out of
the event.
[0060] The output from the Resource Modeler 108 is fed into the
forecast engine 104. The Forecasting Engine 104 performs short-term
forecasts of aggregate load and available load-sheds for individual
loads connected to DROMS-RT based on the list of available
resources and the participating loads. DROMS-RT 100, by providing a
unified view of all DR resources across all programs and optimally
dispatching these resources will make the system significantly more
efficient. This efficiency gain will bring down the cost of the
electricity for customers and will further spur adoption of the
technology causing a powerful positive feedback loop.
[0061] Historical time-series data from past participation will be
used to build a self-calibrated model for each customer that will
be able to forecast, shed capacity, ramp time and rebound effects
for that customer given the time-of-day, weather and price
signal.
[0062] The portfolio of dynamic resources is controlled by DROMS-RT
to produce pseudo generation per utility ISO signal. The DROMS-RT
load forecast model also takes into account customer specific
behavior and is able to predict the change in load profile due to
the use of specific strategies that the customer might be using to
shift the load from a period of high prices to low prices. Such
strategies may include pre-cooling a building in anticipation of
high prices to reduce the usage during the higher priced events.
Such strategies are implicitly `learned` from the load time-series
of individual customers. The DROMS-RT load forecast algorithm is
also able to predict effects of customer fatigue when the high
price events happen repeatedly, back-to-back or if they are too
long, by looking at the customer load data. The availability of the
individual load forecasts also improve the overall accuracy of the
system-level load forecast by combining many different bottom-up
sources of data.
[0063] The system 100 of the present invention is cost-effective,
reliable and stable for accurate real-time forecasting and can be
applied in IP based control and communication telemetry devices and
can be used in individual residences, apartment buildings, offices,
industrial and real-world applications. In addition, the invention
can be coupled with recent advances in ruggedized devices to bring
down the cost for telemetry devices below <$500.
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